Robustness in Statistical Forecasting

1. Front Matter

   Pages i-xvi

   PDF

2. Introduction

   • Yuriy Kharin

   Pages 1-5

3. A Decision-Theoretic Approach to Forecasting

   • Yuriy Kharin

   Pages 7-29

4. Time Series Models of Statistical Forecasting

   • Yuriy Kharin

   Pages 31-53

5. Performance and Robustness Characteristics in Statistical Forecasting

   • Yuriy Kharin

   Pages 55-72

6. Forecasting Under Regression Models of Time Series

   • Yuriy Kharin

   Pages 73-104

7. Robustness of Time Series Forecasting Based on Regression Models

   • Yuriy Kharin

   Pages 105-162

8. Optimality and Robustness of ARIMA Forecasting

   • Yuriy Kharin

   Pages 163-230

9. Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values

   • Yuriy Kharin

   Pages 231-272

10. Robustness of Multivariate Time Series Forecasting Based on Systems of Simultaneous Equations

    • Yuriy Kharin

    Pages 273-303

11. Forecasting of Discrete Time Series

    • Yuriy Kharin

    Pages 305-352

12. Back Matter

    Pages 353-356

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Traditional procedures in the statistical forecasting of time series, which are proved to be optimal under the hypothetical model, are often not robust under relatively small distortions (misspecification, outliers, missing values, etc.), leading to actual forecast risks (mean square errors of prediction) that are much higher than the theoretical values. This monograph fills a gap in the literature on robustness in statistical forecasting, offering solutions to the following topical problems:

- developing mathematical models and descriptions of typical distortions in applied forecasting problems;

- evaluating the robustness for traditional forecasting procedures under distortions;

- obtaining the maximal distortion levels that allow the “safe” use of the traditional forecasting algorithms;

- creating new robust forecasting procedures to arrive at risks that are less sensitive to definite distortion types.                

• 62-02, 62M20, 62M10, 62G35, 62-07, 62F35, 62C20, 62P20
• forecasting
• model distortion
• risk
• robustness
• time series

From the reviews:

“The book is intended for mathematicians, statisticians and software developers in applied mathematics, computer science, data analysis, and econometrics, among other topics. It is a good text for advanced undergraduate and postgraduate students of the mentioned disciplines.” (Oscar Bustos, zbMATH, Vol. 1281, 2014)

• Department of Mathematical Modeling and Data Analysis, Belarusian State University, Minsk, Belarus

  Yuriy Kharin

Yuriy Kharin is Chairman of the Department of Mathematical Modeling & Data Analysis, Director of the Research Institute for Applied Problems of Mathematics & Informatics at the Belarusian State University. He completed his Ph.D. in Math. Sci. at the Tomsk State University in 1974 and his Dr. Sci. in Math. Sci. at the USSR Academy of Sciences in 1986. His research interests include mathematical and applied statistics, robust statistics, and statistical forecasting. He is founder and first President of the Belarusian Statistical Association (1998), Laureate of National Science Prize (2002), and a Correspondent Member of the National Academy of Sciences of Belarus (2004).

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